Detect & nerf AI-driven follower bots — label, filter, and rate-limit them

Executive Summary

Creators, brands and public figures—roughly 50 million accounts in aggregate—are increasingly harmed by AI-driven follower bots that inflate vanity metrics, distort engagement signals and erode monetization and brand safety. The practical consequences are missed sponsorship revenue, worse ad targeting and reputational risk when inauthentic audiences are amplified. You could build a cross-platform SaaS that detects, labels and mitigates follower bots with a transparent per-account "trust score," configurable filters and rate-limiting controls exposed via API, dashboard and browser/plugin integrations. The detection stack would combine behavioral graph analysis, device/browser heuristics, ML models tuned for adversarial examples and human-in-the-loop review, sold to creators, agencies and brands with a target ARPU around $240/year. This is an attractive moment: the addressable market is roughly $12.0B (50M creators × $240 ARPU/year), the Market Score is 92/100 and Revenue Potential 88/100, driven by cheaper, more believable AI bots and platform API flux that forces third-party cross-platform solutions. Creators and advertisers face immediate commercial pressure to clean engagement signals to protect payouts and campaign ROI. To stand out you must deliver high-precision, explainable signals and frictionless integrations so customers can see, act on and appeal bot labels across platforms, and offer enterprise features like SLAs, audit logs and bulk remediation. Key challenges are an adversarial arms race, shifting API access and privacy/regulatory constraints, which mean success requires continuous R&D, data partnerships and a clear, conservative approach to false positives.